A Bayesian model for ranking hazardous sites

Road safety has recently become a major concern in most modern societies. The determination of sites that are more dangerous than others (black spots) can help in better scheduling road safety policies. The present paper proposes a methodology to rank sites according to their hazardousness. The model is innovative in at least two respects. Firstly, it makes use of all relevant information per accident location, including the total number of accidents, the number of fatalities, as well as the number of both light and severe injuries. Secondly, the model includes the use of a cost function to rank the sites with respect to their total expected cost to the society. Bayesian estimation for the model via a Markov Chain Monte Carlo (MCMC) approach is proposed. Accident data from 519 intersections in Leuven (Belgium) are used to illustrate the proposed methodology. Furthermore, different cost functions are used in the paper in order to show the sensitivity of the proposed method on the use of different costs per injury type.

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